Overview

Dataset statistics

Number of variables9
Number of observations768
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.1 KiB
Average record size in memory72.2 B

Variable types

Numeric8
Categorical1

Warnings

Pregnancies has 111 (14.5%) zeros Zeros
BloodPressure has 35 (4.6%) zeros Zeros
SkinThickness has 227 (29.6%) zeros Zeros
Insulin has 374 (48.7%) zeros Zeros
BMI has 11 (1.4%) zeros Zeros

Reproduction

Analysis started2021-04-27 03:22:43.530997
Analysis finished2021-04-27 03:22:52.155720
Duration8.62 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Pregnancies
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.845052083
Minimum0
Maximum17
Zeros111
Zeros (%)14.5%
Memory size6.1 KiB
2021-04-26T23:22:52.241528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.369578063
Coefficient of variation (CV)0.8763413316
Kurtosis0.1592197775
Mean3.845052083
Median Absolute Deviation (MAD)2
Skewness0.9016739792
Sum2953
Variance11.35405632
MonotocityNot monotonic
2021-04-26T23:22:52.325297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1135
17.6%
0111
14.5%
2103
13.4%
375
9.8%
468
8.9%
557
7.4%
650
 
6.5%
745
 
5.9%
838
 
4.9%
928
 
3.6%
Other values (7)58
7.6%
ValueCountFrequency (%)
0111
14.5%
1135
17.6%
2103
13.4%
375
9.8%
468
8.9%
ValueCountFrequency (%)
171
 
0.1%
151
 
0.1%
142
 
0.3%
1310
1.3%
129
1.2%

Glucose
Real number (ℝ≥0)

Distinct136
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.8945312
Minimum0
Maximum199
Zeros5
Zeros (%)0.7%
Memory size6.1 KiB
2021-04-26T23:22:52.428028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79
Q199
median117
Q3140.25
95-th percentile181
Maximum199
Range199
Interquartile range (IQR)41.25

Descriptive statistics

Standard deviation31.9726182
Coefficient of variation (CV)0.2644670347
Kurtosis0.6407798204
Mean120.8945312
Median Absolute Deviation (MAD)20
Skewness0.1737535018
Sum92847
Variance1022.248314
MonotocityNot monotonic
2021-04-26T23:22:52.533746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10017
 
2.2%
9917
 
2.2%
12914
 
1.8%
12514
 
1.8%
11114
 
1.8%
10614
 
1.8%
9513
 
1.7%
10813
 
1.7%
10513
 
1.7%
10213
 
1.7%
Other values (126)626
81.5%
ValueCountFrequency (%)
05
0.7%
441
 
0.1%
561
 
0.1%
572
 
0.3%
611
 
0.1%
ValueCountFrequency (%)
1991
 
0.1%
1981
 
0.1%
1974
0.5%
1963
0.4%
1952
0.3%

BloodPressure
Real number (ℝ≥0)

ZEROS

Distinct47
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.10546875
Minimum0
Maximum122
Zeros35
Zeros (%)4.6%
Memory size6.1 KiB
2021-04-26T23:22:52.639466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38.7
Q162
median72
Q380
95-th percentile90
Maximum122
Range122
Interquartile range (IQR)18

Descriptive statistics

Standard deviation19.35580717
Coefficient of variation (CV)0.2800908166
Kurtosis5.18015656
Mean69.10546875
Median Absolute Deviation (MAD)8
Skewness-1.843607983
Sum53073
Variance374.6472712
MonotocityNot monotonic
2021-04-26T23:22:52.745184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
7057
 
7.4%
7452
 
6.8%
6845
 
5.9%
7845
 
5.9%
7244
 
5.7%
6443
 
5.6%
8040
 
5.2%
7639
 
5.1%
6037
 
4.8%
035
 
4.6%
Other values (37)331
43.1%
ValueCountFrequency (%)
035
4.6%
241
 
0.1%
302
 
0.3%
381
 
0.1%
401
 
0.1%
ValueCountFrequency (%)
1221
 
0.1%
1141
 
0.1%
1103
0.4%
1082
0.3%
1063
0.4%

SkinThickness
Real number (ℝ≥0)

ZEROS

Distinct51
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.53645833
Minimum0
Maximum99
Zeros227
Zeros (%)29.6%
Memory size6.1 KiB
2021-04-26T23:22:52.849901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q332
95-th percentile44
Maximum99
Range99
Interquartile range (IQR)32

Descriptive statistics

Standard deviation15.95221757
Coefficient of variation (CV)0.776775494
Kurtosis-0.5200718662
Mean20.53645833
Median Absolute Deviation (MAD)12
Skewness0.1093724965
Sum15772
Variance254.4732453
MonotocityNot monotonic
2021-04-26T23:22:52.953623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0227
29.6%
3231
 
4.0%
3027
 
3.5%
2723
 
3.0%
2322
 
2.9%
3320
 
2.6%
1820
 
2.6%
2820
 
2.6%
3119
 
2.5%
3918
 
2.3%
Other values (41)341
44.4%
ValueCountFrequency (%)
0227
29.6%
72
 
0.3%
82
 
0.3%
105
 
0.7%
116
 
0.8%
ValueCountFrequency (%)
991
0.1%
631
0.1%
601
0.1%
561
0.1%
542
0.3%

Insulin
Real number (ℝ≥0)

ZEROS

Distinct186
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.79947917
Minimum0
Maximum846
Zeros374
Zeros (%)48.7%
Memory size6.1 KiB
2021-04-26T23:22:53.056352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30.5
Q3127.25
95-th percentile293
Maximum846
Range846
Interquartile range (IQR)127.25

Descriptive statistics

Standard deviation115.2440024
Coefficient of variation (CV)1.444169856
Kurtosis7.214259554
Mean79.79947917
Median Absolute Deviation (MAD)30.5
Skewness2.272250858
Sum61286
Variance13281.18008
MonotocityNot monotonic
2021-04-26T23:22:53.253824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0374
48.7%
10511
 
1.4%
1409
 
1.2%
1309
 
1.2%
1208
 
1.0%
1007
 
0.9%
947
 
0.9%
1807
 
0.9%
1106
 
0.8%
1156
 
0.8%
Other values (176)324
42.2%
ValueCountFrequency (%)
0374
48.7%
141
 
0.1%
151
 
0.1%
161
 
0.1%
182
 
0.3%
ValueCountFrequency (%)
8461
0.1%
7441
0.1%
6801
0.1%
6001
0.1%
5791
0.1%

BMI
Real number (ℝ≥0)

ZEROS

Distinct248
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.99257812
Minimum0
Maximum67.1
Zeros11
Zeros (%)1.4%
Memory size6.1 KiB
2021-04-26T23:22:53.356546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.8
Q127.3
median32
Q336.6
95-th percentile44.395
Maximum67.1
Range67.1
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation7.88416032
Coefficient of variation (CV)0.2464371671
Kurtosis3.290442901
Mean31.99257812
Median Absolute Deviation (MAD)4.6
Skewness-0.4289815885
Sum24570.3
Variance62.15998396
MonotocityNot monotonic
2021-04-26T23:22:53.460268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3213
 
1.7%
31.612
 
1.6%
31.212
 
1.6%
011
 
1.4%
33.310
 
1.3%
32.410
 
1.3%
32.89
 
1.2%
30.89
 
1.2%
32.99
 
1.2%
30.19
 
1.2%
Other values (238)664
86.5%
ValueCountFrequency (%)
011
1.4%
18.23
 
0.4%
18.41
 
0.1%
19.11
 
0.1%
19.31
 
0.1%
ValueCountFrequency (%)
67.11
0.1%
59.41
0.1%
57.31
0.1%
551
0.1%
53.21
0.1%

DiabetesPedigreeFunction
Real number (ℝ≥0)

Distinct517
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4718763021
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2021-04-26T23:22:53.567852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.14035
Q10.24375
median0.3725
Q30.62625
95-th percentile1.13285
Maximum2.42
Range2.342
Interquartile range (IQR)0.3825

Descriptive statistics

Standard deviation0.331328595
Coefficient of variation (CV)0.7021513764
Kurtosis5.594953528
Mean0.4718763021
Median Absolute Deviation (MAD)0.1675
Skewness1.919911066
Sum362.401
Variance0.1097786379
MonotocityNot monotonic
2021-04-26T23:22:53.674566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2546
 
0.8%
0.2586
 
0.8%
0.2595
 
0.7%
0.2385
 
0.7%
0.2075
 
0.7%
0.2685
 
0.7%
0.2615
 
0.7%
0.1674
 
0.5%
0.194
 
0.5%
0.274
 
0.5%
Other values (507)719
93.6%
ValueCountFrequency (%)
0.0781
0.1%
0.0841
0.1%
0.0852
0.3%
0.0882
0.3%
0.0891
0.1%
ValueCountFrequency (%)
2.421
0.1%
2.3291
0.1%
2.2881
0.1%
2.1371
0.1%
1.8931
0.1%

Age
Real number (ℝ≥0)

Distinct52
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.24088542
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2021-04-26T23:22:53.778289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q341
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.76023154
Coefficient of variation (CV)0.3537881556
Kurtosis0.6431588885
Mean33.24088542
Median Absolute Deviation (MAD)7
Skewness1.129596701
Sum25529
Variance138.3030459
MonotocityNot monotonic
2021-04-26T23:22:53.881014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2272
 
9.4%
2163
 
8.2%
2548
 
6.2%
2446
 
6.0%
2338
 
4.9%
2835
 
4.6%
2633
 
4.3%
2732
 
4.2%
2929
 
3.8%
3124
 
3.1%
Other values (42)348
45.3%
ValueCountFrequency (%)
2163
8.2%
2272
9.4%
2338
4.9%
2446
6.0%
2548
6.2%
ValueCountFrequency (%)
811
0.1%
721
0.1%
701
0.1%
692
0.3%
681
0.1%

Outcome
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
0
500 
1
268 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters768
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
0500
65.1%
1268
34.9%
2021-04-26T23:22:54.048566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-26T23:22:54.099430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0500
65.1%
1268
34.9%

Most occurring characters

ValueCountFrequency (%)
0500
65.1%
1268
34.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number768
100.0%

Most frequent character per category

ValueCountFrequency (%)
0500
65.1%
1268
34.9%

Most occurring scripts

ValueCountFrequency (%)
Common768
100.0%

Most frequent character per script

ValueCountFrequency (%)
0500
65.1%
1268
34.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII768
100.0%

Most frequent character per block

ValueCountFrequency (%)
0500
65.1%
1268
34.9%

Interactions

2021-04-26T23:22:45.660382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:45.808988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:45.924679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:46.053335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:46.162044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:46.272748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:46.375474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:46.479206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:46.589946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:46.694666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:46.796395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:46.899086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:47.015774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:47.131464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:47.246158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:47.415705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:47.535385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:47.642099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:47.749811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:47.860515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:47.973214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:48.090920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:48.207608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:48.317314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:48.424029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:48.523783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:48.633490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:48.741202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:48.846919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:48.953634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:49.062343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:49.164071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:49.266797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:49.371537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:49.474262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:49.575990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:49.680744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:49.779480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:49.874230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:49.966984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:50.058734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:50.158468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:50.254370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:50.418932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:50.518668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:50.640304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:50.753026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:50.867719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:50.983408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:51.090123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:51.203819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:51.320507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:51.429216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:51.529947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:51.626688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-26T23:22:51.726422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-04-26T23:22:54.161265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-26T23:22:54.292913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-26T23:22:54.436529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-26T23:22:54.576156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-04-26T23:22:51.902149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-26T23:22:52.063934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
061487235033.60.627501
11856629026.60.351310
28183640023.30.672321
318966239428.10.167210
40137403516843.12.288331
55116740025.60.201300
637850328831.00.248261
71011500035.30.134290
82197704554330.50.158531
9812596000.00.232541

Last rows

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
7581106760037.50.197260
7596190920035.50.278661
76028858261628.40.766220
76191707431044.00.403431
762989620022.50.142330
76310101764818032.90.171630
76421227027036.80.340270
7655121722311226.20.245300
7661126600030.10.349471
7671937031030.40.315230